AI-102 Practice Test 32 — 15 Questions

Question 1

You are building a custom entity extraction solution using Azure AI Language. You have a small dataset (50 documents) with annotated entities. You need to train a model that can extract similar entities from new documents. What is the best approach?

Accepted Answer

Create a custom NER project in Azure AI Language and train it with your annotated data.. Option C is correct because Custom NER allows you to train a model with a small dataset, especially if you use active learning to improve. Option A is wrong because the prebuilt entity recognition does not cover custom entities. Option B is wrong because the built-in extractor skills in AI Search are for indexing, not for custom entity extraction. Option D is wrong because the Conversational PII entity extraction is for PII only.

Answer

Use the prebuilt entity recognition API to extract entities.

Answer

Use the Conversational PII entity extraction feature.

Answer

Use the built-in entity extraction skill in Azure AI Search.

Question 2

Which TWO capabilities are provided by Azure AI Language's pre-built entity recognition?

Accepted Answer

Identifying names of people. Options A and D are correct. Pre-built entity recognition can identify people (A) and organizations (D). Option B is wrong because custom entities require custom NER. Option C is wrong because key phrase extraction is a separate feature. Option E is wrong because sentiment analysis is a separate feature.

Answer

Identifying domain-specific medical terms

Answer

Extracting key phrases from text

Answer

Determining overall sentiment of the text

Question 3

Refer to the exhibit. You send this request to the Azure AI Language Service for custom entity recognition. The response returns no entities. What is the most likely reason?

Accepted Answer

The projectName and deploymentName are missing from the body parameters. The correct answer is D because the request is missing the 'projectName' and 'deploymentName' under the 'parameters' of the body. The exhibit shows them at the top level but not inside the body parameters. The API requires these inside the body. A (language mismatch) is not the issue because 'en' is correct. B (text too short) is not the cause. C (model version) is set to 'latest'.

Answer

The text input is too short for entity recognition

Answer

The language parameter is set incorrectly

Answer

The model version is not specified

Question 4

You are a developer at a global e-commerce company. You are building a multilingual chatbot using Azure AI Language that supports English, French, German, and Spanish. The chatbot must answer frequently asked questions about order status, returns, and shipping. You plan to use Custom Question Answering with a single project containing questions and answers in all four languages. However, during testing, you notice that queries in French and German often return incorrect answers or no answer, while English and Spanish work well. You need to ensure accurate answers across all four languages. What should you do?

Accepted Answer

Create a separate Custom Question Answering project for each language and route user queries to the appropriate project based on language detection.. Custom Question Answering is language-specific. Each project is optimized for a single language. Mixing multiple languages in one project degrades performance because the model's language detection and matching algorithms are tuned for one language. The correct approach is to create separate projects per language and route queries based on detected language. Option A is correct. Option B is incorrect because adding synonyms does not solve the fundamental language mismatch. Option C is incorrect because the same data will still be mixed. Option D is incorrect because Cognitive Search does not provide the same QnA matching capabilities.

Answer

Use Azure Cognitive Search with semantic ranking to index the QnA pairs.

Answer

Add synonyms in the project for French and German terms to improve matching.

Answer

Deploy the same project to multiple regions and use traffic manager.

Question 5

Your company uses Azure AI Language to analyze customer feedback. You need to extract key phrases from reviews in multiple languages. Which feature should you use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct feature for identifying main points in text. Sentiment analysis detects positive/negative tone, entity recognition identifies named entities, and language detection identifies the language.

Answer

Named entity recognition

Answer

Language detection

Answer

Sentiment analysis

Question 6

You are developing a chatbot for a retail company using Azure AI Language's custom question answering. The chatbot must provide answers from a knowledge base of 500 FAQ documents. Users often ask the same question in different wording, and the chatbot fails to return an answer for paraphrased queries. What is the most effective solution?

Accepted Answer

Enable active learning in the project settings and periodically publish the updated knowledge base.. Option C is correct because enabling active learning allows the system to learn from user interactions and suggest alternative phrasings. Option A is wrong because increasing document count doesn't help with paraphrasing. Option B is wrong because additional training is not a built-in feature; active learning is. Option D is wrong because Azure AI Bot Service is for bot hosting, not improving answer coverage.

Answer

Use Azure AI Bot Service's Direct Line Speech channel to improve accuracy.

Answer

Increase the number of FAQ documents in the knowledge base.

Answer

Manually add alternate question phrases to the knowledge base for each QnA pair.

Question 7

A developer is building a multilingual chatbot using Azure AI Language. The bot must detect the user's language automatically and route the query to the appropriate language-specific model. Which Azure AI Language feature should the developer use?

Accepted Answer

Language detection API.. Option A is correct because language detection identifies the language of the input text, allowing routing to the correct model. Option B is wrong because translation changes the text but does not detect language. Option C is wrong because the multilingual CLU model handles multiple languages in one project but does not detect language. Option D is wrong because the custom text classification model is for classification, not language detection.

Answer

Translator API.

Answer

Conversational language understanding (CLU) with multilingual project.

Answer

Custom text classification model.

Question 8

Refer to the exhibit. You are calling the Azure AI Language API for conversational language understanding (CLU). The CLU project 'SupportBot' has an intent 'CancelOrder' with an entity 'OrderNumber' of type 'Number'. The deployment 'production' is active. What is the expected output?

Accepted Answer

The response will contain the top intent 'CancelOrder' and the entity 'OrderNumber' with value '12345'.. Option D is correct because the CLU model will predict the 'CancelOrder' intent and extract the entity 'OrderNumber' with value '12345'. Option A is wrong because the intent is indeed recognized. Option B is wrong because the entity is extracted. Option C is wrong because the entity is correctly extracted as a number.

Answer

The response will contain an error because the deployment is not active.

Answer

The response will contain the entity 'OrderNumber' with value '#12345' because the hash is part of the entity.

Answer

The response will contain only the top intent, but no entities because the entity type is not recognized.

Question 9

A company uses Azure AI Language Service with Custom Entity Recognition to extract invoice fields. The model correctly extracts invoice numbers but fails to extract dates in the format 'dd/mm/yyyy'. The training data includes dates in 'mm/dd/yyyy' format. What is the most likely issue?

Accepted Answer

The training data does not contain examples with the 'dd/mm/yyyy' format. The correct answer is C because the model learns the date format from training data; if training data uses a different format, it will not recognize other formats. A (entity length) is not the issue. B (overfitting) would still recognize dates if format matches. D (language detection) is irrelevant.

Answer

The dates exceed the maximum entity length

Answer

The language detection is incorrectly identifying the locale

Answer

The model is overfitting to invoice numbers

Question 10

A financial services company uses Azure AI Language's custom text classification to categorize loan applications as 'Approved', 'Denied', or 'Review Required'. The model is trained on historical data but is producing poor accuracy on new applications. The data scientist suspects data leakage between training and test sets. What should the data scientist do to validate this?

Accepted Answer

Split the data chronologically and ensure no overlapping data between train and test sets.. Option B is correct because evaluating the model with a holdout set that was never used in training is the standard way to detect data leakage. Option A is wrong because more training data won't fix leakage. Option C is wrong because cross-validation can still have leakage if leakage is in the data splitting. Option D is wrong because adjusting confidence thresholds doesn't fix leakage.

Answer

Increase the training dataset size and retrain the model.

Answer

Use k-fold cross-validation during training.

Answer

Adjust the classification confidence threshold.

Question 11

A company uses Azure AI Language's conversational language understanding (CLU) to build a customer support bot. They want to integrate the bot with Microsoft Teams and need to ensure that user authentication is handled by Microsoft Entra ID. However, users report that the bot sometimes fails to respond when they are not signed into Microsoft Entra ID. What is the most likely cause?

Accepted Answer

The bot's authentication settings in Azure Bot Service require Microsoft Entra ID, but the bot is not passing the token correctly.. Option C is correct because the bot's authentication settings may require Microsoft Entra ID, but if the bot is configured to expect tokens from Microsoft Entra ID and users are not signed in, the bot cannot authenticate requests. Option A is wrong because the CLU project endpoint does not require authentication for each query; authentication is handled at the bot level. Option B is wrong because the CLU model does not require a Microsoft Entra ID token for prediction. Option D is wrong because the bot framework itself does not require Microsoft Entra ID; it can use other authentication methods.

Answer

The CLU model requires a Microsoft Entra ID token for prediction.

Answer

The CLU project endpoint is not configured to accept anonymous requests.

Answer

The bot is not registered in Microsoft Entra ID.

Question 12

A company uses Azure AI Language Service to analyze customer feedback. They notice that the sentiment scores for negative reviews are often incorrectly labeled as neutral. Which configuration should be adjusted to improve accuracy?

Accepted Answer

Create a custom sentiment analysis model using Custom Text Classification. The correct answer is C because sentiment analysis accuracy can be improved by providing a custom model trained on domain-specific data. A (increasing confidence threshold) would reduce false positives but not address mislabeling. B (changing to a different region) does not affect model accuracy. D (using key phrase extraction) is unrelated to sentiment.

Answer

Deploy the Language service in a different Azure region

Answer

Increase the confidence threshold for sentiment classification

Answer

Enable Key Phrase Extraction to preprocess the text

Question 13

Which THREE components are required to build a custom question-answering solution using Azure AI Language Service?

Accepted Answer

A bot channel registration or web app to interact with users. The correct answers are A, C, and E. A provides the knowledge base. C is the service required. E provides the interface for users to ask questions. B (Azure AI Search) is not required because Custom Question Answering has its own index. D (Azure Functions) is optional.

Answer

An Azure AI Search index

Answer

An Azure Functions app for serverless compute

Question 14

A news organization wants to automatically summarize long articles into short, coherent summaries. The solution must preserve the original meaning and key points. Which Azure AI service should be used?

Accepted Answer

Azure AI Language - Extractive Summarization. Option C is correct because Azure AI Language's extractive summarization extracts key sentences from the text. Option A is wrong because Azure AI Translator is for translation. Option B is wrong because Azure AI Language's key phrase extraction extracts phrases, not summaries. Option D is wrong because Azure AI Document Intelligence is for form data extraction.

Answer

Azure AI Document Intelligence

Answer

Azure AI Language - Key Phrase Extraction

Answer

Azure AI Translator

Question 15

Refer to the exhibit. You submit this request to the Azure AI Language service. What is the expected response?

Accepted Answer

Both entity recognition and key phrase extraction results.. Option C is correct. The request includes two tasks: entity recognition and key phrase extraction. The response will contain both sets of results. Option A is wrong because entity recognition is included. Option B is wrong because key phrase extraction is included. Option D is wrong because the request is valid.

Answer

An error because the request format is invalid.

Answer

Only key phrase extraction results.

Answer

Only entity recognition results.